Abstract
The performance of a layered manufacturing (LM) process is determined by the appropriate setting of process parameters. The study of the relationship between performance and process parameters is therefore an important area of LM process planning research. The trend in modern industry is to move from conventional automation to intelligent automation. LM technology is essentially an automated manufacturing technology that is evolving towards an intelligent automation technology. Slicing solid manufacturing (SSM) is a LM technique using paper as the working material and a CO2 laser as the cutting tool. In this manuscript, a back propagation (BP) learning algorithm of an artificial neural network (ANN) is used to determine appropriate process parameters for the SSM method. Key process parameters affecting accuracy are investigated. Quantitative relationships between the input parameters and output accuracy are established by developing the BP neural network.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
